Recently, contrastive predictive coding [1]}, [2]} approaches offer better representational abilities than traditional deep learning models and are hence, good candidates to learn from MVTS [3]}. However, their ability to represent temporal and channel correlations for anomaly detection is not fully known. In this paper, we propose a Time-series Representational Learning through Contrastive Predictive Coding (TRL-CPC) model for anomaly detection from MVTS REF . The TRL-CPC comprises of an encoder, an auto-regressor, and a non-linear transformation function. First, the encoder and the auto-regressor learns the representation of the sequence of the time-series in the observation window to generate the context vectors. The nonlinear transformation function, which can be approximated through a single hidden layer perceptron network, then transforms these context vectors to estimate the latent vectors for the sequence of time-series in the prediction window. This estimated latent vectors is then contrasted with the actual latent vectors from the encoder for better representation of the relationship between the observation and prediction window. All the three components, viz., the encoder, the auto-regressor and the non-linear transformation function are jointly optimized such that the density of the positive pair (time-series sequence of observation Vs prediction window) is maximized. We demonstrate the anomaly detection capabilities of the proposed TRL-CPC on 2 publicly available MVTS data set, namely, Skoltech Anomaly benchmark [4]} and WADI [5]} with \(36.02\) % and \(5.79\) % of anomalies, respectively. These data sets are chosen with extremities in the anomaly %. Finally, we apply the proposed method on a real-world data set. Performance studies show the superior anomaly detection capabilities of the proposed TRL-CPC.
